Novel Methods and Technologies for Intelligent Vehicles (Volume II)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 3388

Special Issue Editors

Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430070, China
Interests: intelligent vehicle; artificial intelligence; traffic safety; vehicle behavior recognition; machine learning; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals
School of Management, Wuhan University of Technology, Wuhan 430070, China
Interests: big data analytics in sustainable operations management; socially-responsible AI; green technological innovation; eco-friendly transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent vehicle (IV) is a comprehensive system that integrates functions such as environment perception, planning, and decision making, and multi-level assisted driving. It concentrates on the technologies of computer, modern sensing, information fusion, communication, artificial intelligence, and automatic control, etc.

The improvement of the intelligence level of IV can enhance traffic safety and efficiency effectively. In recent years, with the development of hardware and software, the technology of Intelligent Connected Vehicle (ICV) has achieved rapid progress. However, there are many critical and difficult issues that remain to be addressed.

This special issue is dedicated to papers focusing on novel methods and technologies for intelligent vehicles. Researchers from both academia and industry are welcomed to submit unpublished research work related to the perception, decision-making and control, and other key technologies of IV. This includes but is not limited to the following: Intelligent Connected Vehicles; Artificial Intelligence; Navigation and Localization; Environmental Perception; Cooperative Vehicle Infrastructure Systems; Data Fusion; Driver-autonomous Integration Cooperative Driving; Decision-making and Planning; Vehicle Dynamics Control.

Dr. Zhijun Chen
Dr. Yishi Zhang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent connected vehicles
  • artificial intelligence
  • navigation and localization
  • environmental perception
  • cooperative vehicle infrastructure systems
  • data fusion
  • driver-autonomous integration cooperative driving
  • decision-making and planning
  • vehicle dynamics control

Published Papers (3 papers)

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Research

16 pages, 1669 KiB  
Article
Spatio-Temporal-Attention-Based Vehicle Trajectory Prediction Considering Multi-Vehicle Interaction in Mixed Traffic Flow
Appl. Sci. 2024, 14(1), 161; https://doi.org/10.3390/app14010161 - 24 Dec 2023
Viewed by 542
Abstract
As a link connecting the environmental perception system and the decision-making system, accurate obstacle trajectory prediction provides a reliable guarantee of correct decision-making by autonomous vehicles. Oriented toward a mixed human-driven and machine-driven traffic environment, a vehicle trajectory prediction algorithm based on an [...] Read more.
As a link connecting the environmental perception system and the decision-making system, accurate obstacle trajectory prediction provides a reliable guarantee of correct decision-making by autonomous vehicles. Oriented toward a mixed human-driven and machine-driven traffic environment, a vehicle trajectory prediction algorithm based on an encoding–decoding framework composed of a multiple-attention mechanism is proposed. Firstly, a directed graph is used to describe vehicle–vehicle motion dependencies. Then, by calculating the repulsive force between vehicles using a priori edge information based on the artificial potential field theory, vehicle–vehicle interaction coefficients are extracted via a graph attention mechanism (GAT). Subsequently, after concatenating the vehicle–vehicle interaction feature with the encoded vehicle trajectory vectors, a spatio-temporal attention mechanism is applied to determine the coupling relationship of hidden vectors. Finally, the predicted trajectory is generated by a gated recurrent unit (GRU) decoder. The training and evaluation of the proposed model were conducted on the NGSIM public dataset. The test results demonstrated that compared with existing baseline models, our approach has fewer prediction errors and better robustness. In addition, introducing artificial potential fields into the attention mechanism causes the model to have better interpretability. Full article
(This article belongs to the Special Issue Novel Methods and Technologies for Intelligent Vehicles (Volume II))
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26 pages, 4128 KiB  
Article
Blockchain-Based Multistage Continuous Authentication for Smart Devices
Appl. Sci. 2023, 13(23), 12641; https://doi.org/10.3390/app132312641 - 24 Nov 2023
Viewed by 552
Abstract
With the increasing connectivity between multiple smart devices in the Internet of Vehicles, privacy and security have become stringent threats due to unauthorized access. To overcome this issue, designing continuous authentication systems has become an important research topic because of the advantages of [...] Read more.
With the increasing connectivity between multiple smart devices in the Internet of Vehicles, privacy and security have become stringent threats due to unauthorized access. To overcome this issue, designing continuous authentication systems has become an important research topic because of the advantages of continuous monitoring of users after the initial access to the smart devices. Unfortunately, the existing systems are based on a third-party centralized structure, and most of them suffer storage pressure on equipment, thus resulting in significant security hazards and limited performance. In this paper, we propose a multistage continuous authentication system based on blockchain technology and the IPFS, which achieves decentralization and reduces storage pressure. In the first stage of authentication, we adopt Hyperledger Fabric to implement the underlying technical architecture of the blockchain to enhance the security and reliability of identity parameters. The preoutputs of the first-stage authentication are compared against behavioral biometric characteristics stored in the IPFS that aim to accomplish the final authentication. In particular, we use fuzzy extractors to deal with behavioral biometric feature templates, thus solving the privacy problem caused by user information leakage. To evaluate the security of our system, we prove the correctness of the communication protocol and two-way authentication of the scheme using BAN Logic. Furthermore, we use Hyperledger Caliper to analyze the impact of the sending rate of authentication requests on various performance parameters such as throughput, memory, and CPU utilization of the authentication system. Security and experimental results show that: (i) We solve the problem of centralized authentication and can resist replay attacks. (ii) Our scheme can maintain high throughput and effectively reach consensus. Compared to related works, the throughput is improved by 8.6%. Full article
(This article belongs to the Special Issue Novel Methods and Technologies for Intelligent Vehicles (Volume II))
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19 pages, 2288 KiB  
Article
Anomaly Detection for Automated Vehicles Integrating Continuous Wavelet Transform and Convolutional Neural Network
Appl. Sci. 2023, 13(9), 5525; https://doi.org/10.3390/app13095525 - 28 Apr 2023
Cited by 1 | Viewed by 1245
Abstract
Connected and automated vehicles (CAVs) involving massive advanced sensors and electronic control units (ECUs) bring intelligentization to the transportation system and conveniences to human mobility. Unfortunately, these automated vehicles face security threats due to complexity and connectivity. Especially, the existing in-vehicle network protocols [...] Read more.
Connected and automated vehicles (CAVs) involving massive advanced sensors and electronic control units (ECUs) bring intelligentization to the transportation system and conveniences to human mobility. Unfortunately, these automated vehicles face security threats due to complexity and connectivity. Especially, the existing in-vehicle network protocols (e.g., controller area network) lack security consideration, which is vulnerable to malicious attacks and puts people at large-scale severe risks. In this paper, we propose a novel anomaly detection model that integrates a continuous wavelet transform (CWT) and convolutional neural network (CNN) for an in-vehicle network. By transforming in-vehicle sensor signals in different segments, we adopt CWT to calculate wavelet coefficients for vehicle state image construction so that the model exploits both the time and frequency domain characteristics of the raw data, which can demonstrate more hidden patterns of vehicle events and improve the accuracy of the follow-up detection process. Our model constructs a two-dimensional continuous wavelet transform scalogram (CWTS) and utilizes it as an input into our optimized CNN. The proposed model is able to provide local transient characteristics of the signals so that it can detect anomaly deviations caused by malicious behaviors, and the model is effective for coping with various vehicle anomalies. The experiments show the superior performance of our proposed model under different anomaly scenarios. Compared with related works, the average accuracy and F1 score are improved by 2.51% and 2.46%. Full article
(This article belongs to the Special Issue Novel Methods and Technologies for Intelligent Vehicles (Volume II))
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